Man-man yi ci net bi ak yi nekkul ci net bi di joxe Skew Quiz

Sooy def quiz bi, tontu yi ak leeral yi dañuy feeñ.

1. Luy skew tàggat/liggéey?
   • Suñu tàggatee benn model ci GPU waaye ñu koy jëfandikoo ci CPU
   • Su model bi yaatu lool ba mënu ci nekk ci memory bi
   • Sudee man-man yiñ xayma ci net bi ci diiru tàggat-yaram wuute nañu ak yi ñuy joxe ci net bi ci liggéey bi
   • Su tàggat yaram di gëna yàgg
2. When building a historical training row, which feature timestamps should a point-in-time join allow?
   • Values available at or before that row’s prediction time
   • Values recorded only after the outcome
   • The latest values regardless of historical time
   • Only values from the next calendar month
3. Ci li ci topp bii, yan ci ñoom mooy sabab bu gëna bari ci njubte gu bari giy daanu ci liggéey bi?
   • Jëfandikoo misaali tàggat yu néew lool
   • Taggat ci masin bu gëna gaaw liggéey
   • Denc man-man yi ci benn base de done
   • Fuite de données, fu tàggat yaram di jëfandikoo ay leeral yu amul ci waxtu wax luy waaja am
4. A logged live feature distribution differs from training. What should the feature-serving team infer first?
   • The serving code is certainly defective
   • There is a mismatch to investigate; it may be drift or serving skew
   • The feature has no predictive value
   • The model must be running on different hardware
5. Lan moo waral màndarga yiñ denc ci net bi lu melni 'komànd ci waxtu yu mujj yi' nekk risk bu jaarul yoon?
   • Valër biñ denc ci cache bi mën na dem ba yàgg te mënul méngoo ak valeur biñ sooga jëfandikoo ci tàggat yaram
   • Cache dafay gëna rafetal model bi saa yu nekk
   • Cache dafay tax man-man bi mënatul xayma ci offline
   • Man-mani yiñ denc ci cache mënoo nekk ay lim
6. Why share one feature definition across batch training and online serving?
   • To guarantee every cached value is fresh
   • To eliminate changes in incoming populations
   • To make timestamp checks unnecessary
   • To reduce differences caused by separate transformation implementations
7. A transaction model uses a chargeback flag recorded days after purchase. Why is its offline feature join invalid?
   • The flag is categorical rather than numerical
   • The flag was unavailable when the purchase prediction was made
   • Chargeback flags cannot be stored historically
   • Only GPU models may use transaction features
8. Training recomputes a one-hour order count, but serving reads a ten-minute-old cached count. Which check targets this skew?
   • Increase the number of model parameters
   • Remove all timestamps from the feature store
   • Compare counts for matching entities and times while recording cache freshness
   • Switch both paths to a different programming language